贝叶斯LDM:一个域特定的建模语言用于纵向数据的概率建模.
Karine Tung1, Steven De La Torre2, Mohamed El Mistiri3
1University of Massachusetts Amherst, Amherst, MA, USA.
概括
BayesLDM是一个新的贝叶斯纵向数据建模库. 它简化了复杂的时间序列分析,并通过自动生成高效的概率推理代码来加速研究.
科学领域:
- 计算统计的计算统计.
- 机器学习是机器学习.
- 生物统计学 生物统计学
背景情况:
- 纵向数据分析对于理解动态过程至关重要.
- 建模复杂的多变量时间序列带来了重大的计算挑战.
- 现有的方法往往需要广泛的编程专业知识,以有效推断.
研究的目的:
- 介绍BayesLDM,一个用于贝叶斯纵向数据建模的库.
- 为高效的概率推理提供高级建模语言和编译器.
- 为了加速复杂时间序列数据的代建模工作流.
主要方法:
- 开发BayesLDM,一个包含高级建模语言的库.
- 编译器的实现,用于生成优化的概率程序代码.
- 专注于动态贝叶斯网络 (DBN) 的声明性规范.
- 整合模型规范与数据检查以生成推理代码.
主要成果:
- 贝叶斯LDM使DBN的高效,声明式规范成为可能.
- 编译器为贝叶斯推理优化代码,并处理丢失的数据.
- 证明了代建模工作流程的加速.
- 对异质的,部分观察到的移动健康数据的成功应用.
结论:
- 贝叶斯LDM显著简化和加速贝叶斯纵向数据建模.
- 该库抽象了生成高效的概率推理代码的复杂性.
- 贝叶斯LDM对于分析复杂时间序列数据的研究人员来说是一个有价值的工具,特别是在移动健康领域.
相关概念视频
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